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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411
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        About MultiQC

        This report was generated using MultiQC, version 1.30

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/MultiQC/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        Report generated on 2025-08-22, 18:03 UTC based on data in: /home/runner/work/pmultiqc/pmultiqc/data

        pmultiqc

        pmultiqc is a MultiQC module to show the pipeline performance of mass spectrometry based quantification pipelines such as nf-core/quantms, MaxQuant, and DIA-NN.URL: https://github.com/bigbio/pmultiqc


        Results Overview

        Summary Table

        This plot shows the summary statistics of the submitted data.
        This plot shows the summary statistics of the submitted data.
        Showing 1/1 rows and 4/4 columns.
        #MS2 Spectra#Identified MS2 Spectra%Identified MS2 Spectra#Peptides Identified#Proteins Identified
        117692
        116430
        98.93%
        22252
        4512

        HeatMap

        This heatmap provides an overview of the performance of the quantms.
        This plot shows the pipeline performance overview. Some metrics are calculated. * Heatmap score[Contaminants]: as fraction of summed intensity with 0 = sample full of contaminants; 1 = no contaminants * Heatmap score[Pep Intensity (>23.0)]: Linear scale of the median intensity reaching the threshold, i.e. reaching 2^21 of 2^23 gives score 0.25. * Heatmap score[Charge]: Deviation of the charge 2 proportion from a representative Raw file (median). For typtic digests, peptides of charge 2 (one N-terminal and one at tryptic C-terminal R or K residue) should be dominant. Ionization issues (voltage?), in-source fragmentation, missed cleavages and buffer irregularities can cause a shift (see Bittremieux 2017, DOI: 10.1002/mas.21544). * Heatmap score [Missed Cleavages]: the fraction (0% - 100%) of fully cleaved peptides per Raw file * Heatmap score [Missed Cleavages Var]: each Raw file is scored for its deviation from the ‘average’ digestion state of the current study. * Heatmap score [ID rate over RT]: Judge column occupancy over retention time. Ideally, the LC gradient is chosen such that the number of identifications (here, after FDR filtering) is uniform over time, to ensure consistent instrument duty cycles. Sharp peaks and uneven distribution of identifications over time indicate potential for LC gradient optimization.Scored using ‘Uniform’ scoring function. i.e. constant receives good score, extreme shapes are bad. * Heatmap score [MS2 Oversampling]: The percentage of non-oversampled 3D-peaks. An oversampled 3D-peak is defined as a peak whose peptide ion (same sequence and same charge state) was identified by at least two distinct MS2 spectra in the same Raw file. For high complexity samples, oversampling of individual 3D-peaks automatically leads to undersampling or even omission of other 3D-peaks, reducing the number of identified peptides. * Heatmap score [Pep Missing Values]: Linear scale of the fraction of missing peptides.
        Created with MultiQC

        Pipeline Result Statistics

        This plot shows the submitted results
        This plot shows the submitted results. Including the number of identified peptides and the number of identified modified peptides in the submitted results. You can also remove the decoy with the `remove_decoy` parameter.
        Showing 1/1 rows and 2/2 columns.
        Spectra File#Peptide IDs#Protein (group) IDs
        33060_Control_vs_Infection_JMI
        24935
        4512

        Identification Summary

        Number of Peptides identified Per Protein

        This plot shows the number of peptides per protein in the submitted data
        Proteins supported by more peptide identifications can constitute more confident results.
        Created with MultiQC

        ProteinGroups Count

        Number of protein groups per raw file.
        Based on statistics calculated from mzTab, mzIdentML (mzid), or DIA-NN report files.
        Created with MultiQC

        Peptide ID Count

        Number of unique (i.e. not counted twice) peptide sequences including modifications per Raw file.
        Based on statistics calculated from mzTab, mzIdentML (mzid), or DIA-NN report files.
        Created with MultiQC

        MS/MS Identified Per Raw File

        MS/MS identification rate per Raw file.
        MS/MS identification rate per raw file (quantms data from mzTab and mzML files; MaxQuant data from summary.txt)
        Created with MultiQC

        Quantification Analysis

        Peptides Quantification Table

        This plot shows the quantification information of peptides in the final result (mzIdentML).
        The quantification information of peptides is obtained from the mzIdentML. The table shows the quantitative level and distribution of peptides in different study variables, run and peptiforms. The distribution show all the intensity values in a bar plot above and below the average intensity for all the fractions, runs and peptiforms. * BestSearchScore: It is equal to max(search_engine_score) for mzIdentML datasets. * Average Intensity: Average intensity of each peptide sequence (0 or NA ignored).
        Showing 50/50 rows and 4/4 columns.
        PeptideIDProtein NamePeptide SequenceBest Search ScoreAverage Intensity
        1
        P37108
        AAAAAAAAAPAAAATAPTTAATTAATAAQ
        3.2800
        5.3110
        2
        P36578
        AAAAAAALQAK
        1.6100
        6.4913
        3
        Q9P258
        AAAAAWEEPSSGNGTAR
        3.1100
        5.6168
        4
        P55036
        AAAASAAEAGIATTGTEDSDDALLK
        5.0000
        5.2036
        5
        Q9NQP4
        AAAEDVNVTFEDQQK
        2.3300
        6.6695
        6
        P07954
        AAAEVNQDYGLDPK
        3.2700
        6.2969
        7
        Q9Y490
        AAAFEEQENETVVVK
        3.9900
        6.0042
        8
        Q96C19
        AAAGELQEDSGLCVLAR
        4.7900
        5.2964
        9
        O14497
        AAAGQESEGPAVGPPQPLGK
        2.4100
        5.1141
        10
        Q8TAE8
        AAALAAAVAQDPAASGAPSS
        1.6900
        5.1676
        11
        P31948
        AAALEFLNR
        2.9300
        5.9891
        12
        P31948
        AAALEFLNRFEEAK
        4.3100
        5.7200
        13
        Q15042
        AAAMTPPEEELK
        2.1500
        5.4049
        14
        P26641
        AAAPAPEEEMDECEQALAAEPK
        4.2600
        5.1253
        15
        P20810
        AAAPAPVSEAVCR
        2.3000
        5.3491
        16
        P31350
        AAAPGVEDEPLLR
        2.8400
        5.5524
        17
        Q16643
        AAAPQAWAGPMEEPPQAQAPPR
        1.8500
        5.4323
        18
        Q9NVA2
        AAAQLLQSQAQQSGAQQTK
        4.9300
        5.3460
        19
        O95801
        AAAQYYLGNFR
        1.3300
        5.3657
        20
        Q9H2M9
        AAASGNENIQPPPLAYK
        1.6700
        5.2086
        21
        Q7Z5L9
        AAASLAAVSGTAAASLGSAQPTDLGAHK
        3.7300
        5.4168
        22
        Q8N1S5
        AAATGLPEGPAVPVPSR
        1.5500
        5.8026
        23
        P49006
        AAATPESQEPQAK
        2.1000
        4.7022
        24
        O43837
        AAAVPVEFQEHHLSEVQNMASEEK
        1.7300
        5.6054
        25
        P13796
        AACLPLPGYR
        2.3200
        6.5701
        26
        Q9UNF0
        AADAVEDLR
        2.3000
        6.2293
        27
        Q9UBQ7
        AADCEVEQWDSDEPIPAK
        4.0800
        5.4222
        28
        P55060
        AADEEAFEDNSEEYIR
        4.5500
        5.4673
        29
        P51452
        AADFIDQALAQK
        2.0800
        5.8593
        30
        Q63HN8
        AADFLSEPEGGPEMAK
        2.6800
        5.1954
        31
        Q9Y277
        AADFQLHTHVNDGTEFGGSIYQK
        5.7600
        5.3097
        32
        Q96ST3
        AADIIDGLR
        1.9600
        5.7119
        33
        O95782
        AADLLYAMCDR
        2.8800
        5.3762
        34
        Q15293
        AADLNGDLTATR
        3.2100
        5.5941
        35
        Q9UBV2
        AADMGNPVGQSGLGMAYLYGR
        3.2900
        5.3300
        36
        Q9BZL4
        AADPGPGAELDPAAPPPAR
        1.3100
        5.0574
        37
        P67809
        AADPPAENSSAPEAEQGGAE
        4.8200
        5.5044
        38
        O60784
        AADRLPNLSSPSAEGPPGPPSGPAPR
        3.8600
        5.8402
        39
        P80723
        AAEAAAAPAESAAPAAGEEPSK
        4.5300
        5.6130
        40
        Q969T9
        AAEAAASAYYNPGNPHNVYMPTSQPPPPPYYPPEDK
        2.8300
        5.3324
        41
        O75569
        AAEAAINILK
        2.2800
        5.6820
        42
        Q08211
        AAECNIVVTQPR
        3.0700
        6.1767
        43
        P06454
        AAEDDEDDDVDTK
        3.3100
        4.5892
        44
        P09496-2
        AAEEAFVNDIDESSPGTEWER
        4.8600
        5.3579
        45
        P42338
        AAEIASSDSANVSSR
        1.8200
        5.2559
        46
        Q9BUK6
        AAELLQDEYSGR
        1.8200
        5.8550
        47
        Q08379
        AAELWGEQAEAR
        2.8100
        5.3849
        48
        P33176
        AAEMMASLLK
        1.9700
        5.7108
        49
        Q10471
        AAEVWMDEYK
        1.9200
        5.4013
        50
        Q9NVZ3
        AAEWQLDQPSWSGR
        3.9800
        5.4308

        Protein Quantification Table

        This plot shows the quantification information of proteins in the final result (mzIdentML).
        The quantification information of proteins is obtained from the mzIdentML. The table shows the quantitative level and distribution of proteins in different study variables and run. * Peptides_Number: The number of peptides for each protein. * Average Intensity: Average intensity of each protein(0 or NA ignored).
        Showing 50/50 rows and 3/3 columns.
        ProteinIDProtein NameNumber of PeptidesAverage Intensity
        1
        A0A0B4J271
        1
        5.3883
        2
        A0A0B4J2D5
        5
        5.9366
        3
        A0A0U1RRE5
        1
        5.1452
        4
        A0A804HLA8
        1
        5.2940
        5
        A0AV96
        4
        5.3425
        6
        A0AVT1
        15
        5.7889
        7
        A0FGR8
        9
        5.6563
        8
        A0MZ66
        8
        5.5114
        9
        A1A4S6
        1
        6.0667
        10
        A1L4H1
        1
        6.0238
        11
        A1X283
        3
        5.5205
        12
        A2A288
        3
        5.3013
        13
        A2A2Z9
        1
        5.8725
        14
        A2RUS2
        6
        5.6169
        15
        A4D1P6
        4
        5.7590
        16
        A4D263
        1
        5.7322
        17
        A5D8V6
        1
        5.9454
        18
        A5D8W1
        1
        6.2870
        19
        A5YKK6
        14
        5.4653
        20
        A6NC98
        7
        5.6973
        21
        A6NCE7
        1
        5.9932
        22
        A6NDG6
        4
        6.0655
        23
        A6NGH7
        1
        6.1800
        24
        A6NHR9
        7
        5.6280
        25
        A6NI72
        11
        5.7315
        26
        A6NMX2
        1
        5.3668
        27
        A6NNS2
        1
        6.3075
        28
        A6NNT2
        1
        6.5198
        29
        A7E2Y1
        1
        6.0884
        30
        A7E2Y1;P35749;P35579;P35579-2;Q7Z406
        1
        6.1558
        31
        A8MWD9
        2
        5.6763
        32
        A8MXV4
        1
        5.1713
        33
        B0I1T2
        8
        5.5747
        34
        C9JLW8
        2
        5.2823
        35
        C9JR72
        1
        4.9286
        36
        E9PAV3
        3
        5.6606
        37
        E9PAV3;Q9BZK3
        1
        5.6568
        38
        G9CGD6
        1
        5.6574
        39
        H7BZ55
        1
        5.0486
        40
        L0R6Q1
        3
        5.3249
        41
        M0R2J8
        1
        5.2644
        42
        O00115
        5
        5.7528
        43
        O00116
        9
        5.6293
        44
        O00139-2
        7
        5.6447
        45
        O00142
        1
        5.8434
        46
        O00148
        3
        5.6010
        47
        O00148;Q13838
        9
        6.0380
        48
        O00151
        3
        5.2950
        49
        O00154
        5
        5.2994
        50
        O00159
        3
        5.3478

        MS2 and Spectral Stats

        Number of Peaks per MS/MS spectrum

        This chart represents a histogram containing the number of peaks per MS/MS spectrum in a given experiment.
        This chart assumes centroid data. Too few peaks can identify poor fragmentation or a detector fault, as opposed to a large number of peaks representing very noisy spectra. This chart is extensively dependent on the pre-processing steps performed to the spectra (centroiding, deconvolution, peak picking approach, etc).
        Created with MultiQC

        Peak Intensity Distribution

        This is a histogram representing the ion intensity vs. the frequency for all MS2 spectra in a whole given experiment. It is possible to filter the information for all, identified and unidentified spectra. This plot can give a general estimation of the noise level of the spectra.
        Generally, one should expect to have a high number of low intensity noise peaks with a low number of high intensity signal peaks. A disproportionate number of high signal peaks may indicate heavy spectrum pre-filtering or potential experimental problems. In the case of data reuse this plot can be useful in identifying the requirement for pre-processing of the spectra prior to any downstream analysis. The quality of the identifications is not linked to this data as most search engines perform internal spectrum pre-processing before matching the spectra. Thus, the spectra reported are not necessarily pre-processed since the search engine may have applied the pre-processing step internally. This pre-processing is not necessarily reported in the experimental metadata.
        Created with MultiQC

        Distribution of Precursor Charges

        This is a bar chart representing the distribution of the precursor ion charges for a given whole experiment.
        This information can be used to identify potential ionization problems including many 1+ charges from an ESI ionization source or an unexpected distribution of charges. MALDI experiments are expected to contain almost exclusively 1+ charged ions. An unexpected charge distribution may furthermore be caused by specific search engine parameter settings such as limiting the search to specific ion charges.
        Created with MultiQC

        Charge-state of Per File

        The distribution of the charge-state of the precursor ion.
        The distribution of the charge-state of the precursor ion.
        Created with MultiQC

        MS/MS Counts Per 3D-peak

        An oversampled 3D-peak is defined as a peak whose peptide ion (same sequence and same charge state) was identified by at least two distinct MS2 spectra in the same Raw file.
        For high complexity samples, oversampling of individual 3D-peaks automatically leads to undersampling or even omission of other 3D-peaks, reducing the number of identified peptides. Oversampling occurs in low-complexity samples or long LC gradients, as well as undersized dynamic exclusion windows for data independent acquisitions.
        Created with MultiQC